Senior AI Platform Engineer - Knowledge & Reasoning
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Key skills for this role
Role Overview
The Esko AI team builds AI capabilities for challenging customer problems that are not well served by rule-based automation.
The role will build shared knowledge and reasoning capabilities for Esko's centralized AI platform and AI-powered packaging workflows.
The position includes end-to-end ownership of substantial technical areas within the platform team.
Key Skills for This Role
Full Job Posting
About the role
The Esko AI team builds AI capabilities for challenging customer problems that are not well served by rule-based automation.
The role will build shared knowledge and reasoning capabilities for Esko's centralized AI platform and AI-powered packaging workflows.
The position includes end-to-end ownership of substantial technical areas within the platform team.
Location and reporting
- The role may be remote, hybrid, or based at an Esko location.
- Worldwide locations are considered.
- The role reports to the Team Leader, Esko AI.
What you will do
- Design knowledge representations for constraints, transformations, procedures, and guidelines that agents, validators, and humans can use.
- Build and operate services for ingesting, curating, retrieving, and versioning customer- and workflow-scoped knowledge.
- Define how retrieval, deterministic validation, and human review handle applicability, uncertainty, conflicts, and escalation.
- Implement reusable APIs and tools that apply, validate, and explain contextual knowledge.
- Build evaluation suites covering retrieval, applicability, conflict, ambiguity, and explanation quality.
- Own security, tenant isolation, reliability, observability, performance, and compatibility for major technical areas.
- Convert domain expertise into structured, testable knowledge with provenance, citations, and release practices.
What we are looking for
- Strong production software engineering experience in Python, TypeScript, Java, or a similar language.
- At least 5 years of software engineering experience, preferably with platform or shared-services ownership.
- Experience building knowledge, retrieval, or rule systems used by other teams.
- Practical experience with RAG, information extraction, and structured validation.
- Sound judgment regarding applicability, exceptions, missing evidence, ambiguity, abstention, and human review.
- Experience constructing evaluation datasets and documenting modeling decisions, tradeoffs, and failure modes.
- An undergraduate, master's, or PhD degree in a relevant computer science, machine learning, data science, artificial intelligence, or related discipline.
- Excellent written and verbal communication with technical and business stakeholders.
Preferred experience
- Semantic and symbolic tooling such as RDF/OWL, SHACL, Datalog, DMN, production rule engines, or policy-as-code.
- Agentic systems with tool calling and validation before action.
- Multi-tenant SaaS with strict isolation and audit requirements.
- Experience in pharmaceutical, food labeling, legal technology, packaging graphics, or the wider graphic arts industry.
Success measures
The knowledge module improves AI agent and service effectiveness using measurable results.
Product teams use shared knowledge services instead of building isolated alternatives.
Agents apply tenant-, market-, and customer-specific constraints, explain influencing knowledge, and escalate when evidence is ambiguous, conflicting, or absent.
Tenant-specific knowledge remains isolated, and the knowledge base is versioned, tested, legible, and curatable.
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